Risk assessment model for invasive breast cancer in Hong Kong women

Wang, Feng; Dai, Juncheng; Li, Mengjie; Chan, Wing-cheong; Kwok, Carol Chi-Hei; Leung, Siu-lan; Wu, Çherry; Li, Wentao; Wang, Yu; Tsang, Koon-Ho; Law, Sze-Hong; Lee, Priscilla Ming Yi; Wong, Carmen Ka-man; Shen, Hongbing; Wong, Samuel Yeung‐Shan; Yang, Xiaohong R.; Tse, Lap Ah

doi:10.1097/md.0000000000004515

Cited by 16 publications

(37 citation statements)

References 38 publications

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“…In agreement with several earlier studies,25–29 postmenopausal women have a higher risk of breast cancer. The results also show agreement with previous models,23,27,29 wherein family history of breast cancer was significantly associated with breast cancer.…”

Section: Discussionsupporting

confidence: 93%

<p>Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study</p>

Ahmed

McClish

Alghamdi

et al. 2019

CMAR

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BackgroundDespite the continuing increase in the breast cancer incidence rate among Saudi Arabian women, no breast cancer risk-prediction model is available in this population. The aim of this research was to develop a risk-assessment tool to distinguish between high risk and low risk of breast cancer in a sample of Saudi women who were screened for breast cancer.MethodsA retrospective chart review was conducted on symptomatic women who underwent breast mass biopsies between September 8, 2015 and November 8, 2017 at King Abdulaziz Medical City, Riyadh, Saudi Arabia.ResultsA total of 404 (63.8%) malignant breast biopsies and 229 (36.2%) benign breast biopsies were analyzed. Women ≥40 years old (aOR: 6.202, CI 3.497–11.001, P=0.001), hormone-replacement therapy (aOR 24.365, 95% CI 8.606–68.987, P=0.001), postmenopausal (aOR 3.058, 95% CI 1.861–5.024, P=0.001), and with a family history of breast cancer (aOR 2.307, 95% CI 1.142–4.658, P=0.020) were independently associated with an increased risk of breast cancer. This model showed an acceptable fit and had area under the receiver-operating characteristic curve of 0.877 (95% CI 0.851–0.903), with optimism-corrected area under the curve of 0.865.ConclusionThe prediction model developed in this study has a high ability in predicting increased breast cancer risk in our facility. Combining information on age, use of hormone therapy, postmenopausal status, and family history of breast cancer improved the degree of discriminatory accuracy of breast cancer prediction. Our risk model may assist in initiating population-screening programs and prompt clinical decision making to manage cases and prevent unfavorable outcomes.

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Section: Discussionsupporting

confidence: 93%

<p>Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study</p>

Ahmed

McClish

Alghamdi

et al. 2019

CMAR

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“…Several other countries in Asia have also developed breast cancer risk models, some of which include additional lifestyle factors and environmental exposures [ 38 39 40 41 42 43 44 ]. Some of the risk factors overlap between models but others do not.…”

Section: Breast Cancer Risk Models and Breast Cancer Risk Prediction mentioning

confidence: 99%

Breast Cancer Risk Prediction in Korean Women: Review and Perspectives on Personalized Breast Cancer Screening

Kim

Park

2020

J Breast Cancer

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Due to an increasing proportion of older individuals and the adoption of a westernized lifestyle, the incidence rate of breast cancer is expected to rapidly increase within the next 10 years in Korea. The National Cancer Screening Program (NCSP) of Korea recommends biennial breast cancer screening through mammography for women aged 40–69 years old and according to individual risk and preference for women above 70 years old. There is an ongoing debate on how to most effectively screen for breast cancer, with many proponents of personalized screening, or screening according to individual risk, for women under 70 years old as well. However, to accurately stratify women into risk categories, further study using more refined personalized characteristics, including potentially incorporating a polygenic risk score (PRS), may be needed. While most breast cancer risk prediction models were developed in Western countries, the Korean Breast Cancer Risk Assessment Tool (KoBCRAT) was developed in 2013, and several other risk models have been developed for Asian women specifically. This paper reviews these models compared to commonly used models developed using primarily Caucasian women, namely, the modified Gail, Breast Cancer Surveillance Consortium, Rosner and Colditz, and Tyrer-Cuzick models. In addition, this paper reviews studies in which PRS is included in risk prediction in Asian women. Finally, this paper discusses and explores strategies toward development and implementation of personalized screening for breast cancer in Korea.

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“…No clarity has been reached so far for patient profiling of preBC resulting in poorer prognosis and higher mortality rates typical for preBC compared to postBC. Moreover, the menopausal status is hardly considered in most currently applied risk assessment models [18]. Consequently, more effective diagnostic approaches, better adapted screening programmes and targeted treatments are of highest priority for research and medical services in the overall BC management.…”

Section: Premenopausal Breast Cancer Contextmentioning

confidence: 99%

“…Accumulated evidence shows that young premenopausal women deal with more aggressive subtypes of BC, have lower survival rates and respond less to conventional chemotherapy, when compared with postmenopausal women [9,[14][15][16][17]. Moreover, a great number of known BC risk factors are dependent on the menopausal status, which, however, is hardly considered in most risk assessment models [18]. Hence, the risks by abnormal (both decreased and increased) BMI are different for premenopausal and postmenopausal women and, further, modulate individual outcomes of the BC metastatic disease [1].…”

Section: Introductionmentioning

confidence: 99%

Premenopausal breast cancer: potential clinical utility of a multi-omics based machine learning approach for patient stratification

et al. 2018

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The study resulted in identifying multi-omic signature specific for the premenopausal BC that can be used for stratifying patients with benign breast alterations. Our predictive model is capable of discriminating individually between high and low BC-risk with high confidence (>90%) and considered of potential clinical utility. Novel risk assessment approaches and advanced screening programmes-as the long-term target of this project-are of particular importance for predictive, preventive and personalised medicine as the medicine of the future, due to the expected health benefits for young subpopulations and the healthcare system as a whole.

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Risk assessment model for invasive breast cancer in Hong Kong women

Abstract: Supplemental Digital Content is available in the text

Cited by 16 publications

References 38 publications

<p>Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study</p>

<p>Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study</p>

Breast Cancer Risk Prediction in Korean Women: Review and Perspectives on Personalized Breast Cancer Screening

Premenopausal breast cancer: potential clinical utility of a multi-omics based machine learning approach for patient stratification

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